Abstract
The performance of unified multimodal models for image generation and editing is fundamentally constrained by the quality and comprehensiveness of their training data. While existing datasets have covered basic tasks like style transfer and simple object manipulation, they often lack the systematic structure and challenging scenarios required for real-world applications. To address this bottleneck, we introduce OpenGPT-4o-Image, a large-scale dataset constructed using a novel methodology that combines hierarchical task taxonomy with automated data generation. Our taxonomy not only includes fundamental capabilities such as text rendering and style control but also introduces highly practical yet challenging categories like scientific imagery for chemistry illustrations and complex instruction editing requiring simultaneous execution of multiple operations. Through an automated pipeline leveraging structured resource pools and GPT-4o, we generate 80k high-quality instruction-image pairs with controlled diversity, covering 11 major domains and 51 subtasks. Extensive experiments show that fine-tuning leading models on our dataset achieves significant performance gains across multiple benchmarks, with improvements of up to 18\% on editing tasks (UniWorld-V1 on ImgEdit-Bench) and 13% on generation tasks (Harmon on GenEval). Our work demonstrates that systematic data construction is key to advancing multimodal AI capabilities.
Abstract (translated)
统一多模态模型在图像生成和编辑方面的性能从根本上受到其训练数据质量和全面性的限制。尽管现有的数据集已经涵盖了风格转移和简单对象操作等基本任务，但它们通常缺乏系统结构和现实应用中所需的挑战性场景。为了解决这一瓶颈问题，我们引入了OpenGPT-4o-Image，这是一个采用新颖方法构建的大规模数据集，该方法结合了层次化的任务分类学与自动数据生成技术。 我们的分类法不仅涵盖了诸如文本渲染和风格控制等基础能力，还引入了一些实用且具有挑战性的类别，例如用于化学插图的科学图像以及需要同时执行多项操作的复杂指令编辑。通过利用结构化资源池和GPT-4o构建的自动化管道，我们生成了80,000对高质量的指令-图像配对，并在控制多样性的情况下涵盖了11个主要领域和51项子任务。 广泛的实验表明，在我们的数据集上进行微调后，领先模型在多个基准测试中取得了显著性能提升：编辑任务中的UniWorld-V1在ImgEdit-Bench上的改进高达18%，而生成任务中Harmon在GenEval上的改进则为13%。我们的研究工作证明了系统化地构建数据对于推进多模态人工智能能力至关重要。
URL
https://arxiv.org/abs/2509.24900
